Derivation and validation of indices incorporating vasopressor dose and blood pressure values over time
Bibliographic record
Abstract
Abstract Rationale The blood pressure value below which the benefits of vasopressors clearly outweigh their disadvantages is uncertain. Objectives The main objective of this analysis was to investigate the statistical properties and potential utility of indices estimating the vasopressor dose-rates as a function of blood pressure values over time. Methods In this single-center observational study, we collected blood pressure values from intensive care unit (ICU) monitors and norepinephrine dose-rates from infusion pumps corresponding to a derivation and a validation cohort. Patients included in each cohort were 18 years or older and received norepinephrine in the ICU. We defined and derived indices corresponding to vasopressor therapy above (>65 mmHg) and below (<60 mmHg) targets. We report the distribution of both indices over time from both cohorts as well as their associations with hospital mortality using logistic regression models adjusted for baseline variables. Results Between July 30 2020 and March 28 2022, 283 patients were included in the derivation cohort. The median ICU stay was 5 days (interquartile range [IQR] 2-10.5) and the median duration of norepinephrine therapy was 27.3 hours (IQR 13.9-54.2). The median cumulative time with a MAP below 60 mmHg was 0.9 hours (IQR 0.2-3.1), whereas the cumulative time with a MAP above 65 mmHg was 22.5 hours (IQR 10.9-40.4). Adjusting for prespecified baseline variables, the above target index was associated with hospital mortality (OR 1.6, 95% CI 1.2 –2.3) but not the below target index (OR 1.1 95% CI 0.8 – 1.5). Between May 5 2022 and December 16 2022, 83 patients were included in the validation cohort. The duration of ICU stay and the proportion of norepinephrine exposure above MAP target were consistent with the derivation cohort. The point estimates of the associations between each index and mortality were also consistent with the derivation cohort, but not statistically significant. Conclusion Indices of vasopressor administration as a function of blood pressure suggest that during most of vasopressor therapy episodes, patients are treated above target. The association of these indices with patient outcomes remains uncertain but could be explored in larger datasets, and future clinical trials of target MAPs may consider measurement of these indices to adjust results for treatment adherence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".